In this paper, we propose a novel approach to detect visual saliency based on spread pattern and manifold ranking. We firstly construct a close-loop graph model with image superpixels as nodes. The saliency of each node is defined by its relevance to given queries according to graph-based manifold ranking technique. Unlike existing methods which choose a few background and foreground queries in a two-stage scheme, we propose to treat each node as a potential foreground query by assigning to it an initial ranking score based on its spread pattern property. The new concept
represents how the ranking score of one node is propagated to the whole graph. An accurate query map is generated accordingly, which is then used to produce the final saliency map with manifold ranking. Our method is computationally efficient and outperforms the state-of-the-art methods.
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